Data split: We group classes into a set of tasks \(\mathcal {T} = \lbrace T_1, \cdots T_t, \cdots \rbrace \) . All the classes of a specific task will be introduced to the system at a point of time \(t\) . While learning \(T_t\) , all the classes of \(\lbrace T_\tau :\tau {<} t\rbrace \)  will be treated as known and \(\lbrace T_\tau :\tau {>} t\rbrace \)  would be treated as unknown. For a concrete instantiation of this protocol, we consider classes from Pascal VOC [1]} and MS-COCO [2]}. We group all VOC classes and data as the first task \(T_1\) . The remaining 60 classes of MS-COCO [2]} are grouped into three successive tasks with semantic drifts (see Tab. REF ).
All images which correspond to the above split from Pascal VOC and MS-COCO train-sets form the training data.
For evaluation, we use the Pascal VOC test split and MS-COCO val split. 1k images from training data of each task is kept aside for validation.
Data splits and codes can be found at https://github.com/JosephKJ/OWOD.
